EvoRecSys: Evolutionary framework for health and well-being recommender systems

Hugo Alcaraz-Herrera's PhD is supported by The Mexican Council of Science and Technology (Consejo Nacional de Ciencia y Tecnologia - CONACyT).In recent years, recommender systems have been employed in domains like ecommerce, tourism, and multimedia streaming, where personalising users’ experience based on their interactions is a fundamental aspect to consider. Recent recommender system developments have also focused on well-being, yet existing solutions have been entirely designed considering one single well-being aspect in isolation, such as a healthy diet or an active lifestyle. This research introduces EvoRecSys, a novel recommendation framework that proposes evolutionary algorithms as the main recommendation engine, thereby modelling the problem of generating personalised well-being recommendations as a multi-objective optimisation problem. EvoRecSys captures the interrelation between multiple aspects of well-being by constructing configurable recommendations in the form of bundled items with dynamic properties. The preferences and a predefined well-being goal by the user are jointly considered. By instantiating the framework into an implemented model, we illustrate the use of a genetic algorithm as the recommendation engine. Finally, this implementation has been deployed as a Web application in order to conduct a users’ study.Consejo Nacional de Ciencia y Tecnologia (CONACyT

Fuzzy rule based profiling approach for enterprise information seeking and retrieval

With the exponential growth of information available on the Internet and various organisational intranets there is a need for profile based information seeking and retrieval (IS&R) systems. These systems should be able to support users with their context-aware information needs. This paper presents a new approach for enterprise IS&R systems using fuzzy logic to develop task, user and document profiles to model user information seeking behaviour. Relevance feedback was captured from real users engaged in IS&R tasks. The feedback was used to develop a linear regression model for predicting document relevancy based on implicit relevance indicators. Fuzzy relevance profiles were created using Term Frequency and Inverse Document Frequency (TF/IDF) analysis for the successful user queries. Fuzzy rule based summarisation was used to integrate the three profiles into a unified index reflecting the semantic weight of the query terms related to the task, user and document. The unified index was used to select the most relevant documents and experts related to the query topic. The overall performance of the system was evaluated based on standard precision and recall metrics which show significant improvements in retrieving relevant documents in response to user queries

Online personalised nutrition advice

The Internet has considerable potential to improve health-related food choice at lowcost. In order to provide online personalised nutrition advice, a valid and user-friendly method for recording dietary intake is key. Yet, the author’s review of popular nutritionrelated mobile apps revealed that none of these apps were capable of providing personalised diet advice This work presents a web app (eNutri), which is able to assess dietary intake using a validated food frequency questionnaire (FFQ) and provide personalised food-based diet advice. The initial version of this app presented the food items in a list and its usability was evaluated in Kuwait. In response to user feedback, the design was modified to present a single food item at a time. This app was deployed in an online study to assess usability with 324 participants in the UK, using different devices. The median System Usability Scale (SUS) score (n=322) was 77.5 (IQR 15.0) out of 100, illustrating high acceptance by users. Potential users were consulted during the design process, but assessing whether nutrition professionals (n=32) agree with the automated advice and collecting their insights were important in maximising the success and wider utility of this app. The mean scores for the appropriateness, relevance and suitability of the eNutri diet messages by nutritional professionals were 3.5, 3.3 and 3.3 respectively (maximum 5). Its effectiveness was evaluated during a 12-week online randomly controlled parallel blinded dietary intervention (n=210) (EatWellUK study) in which personalised dietary advice was compared with general population recommendation (control). A significant improvement in the modified Alternative Healthy Eating Index (m-AHEI) score, against which the participants’ diets were compared, of 3.06 (CI 95% 0.91 to 5.21, p=0.005), was reported following personalised compared to population advice. This work indicates the benefit of personalised dietary advice delivered online to motivate dietary change. The eNutri app’s design and source code were made publicly available under a permissive open source license, so that other researchers and organizations can benefit from this work

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl

Learning from the learners' experience: e-Learning@greenwich post-conference reflections

This publication comprises papers from presenters who, having made a conference presentation, were invited to author an academic paper about their work

Persuasive digital health technologies for lifestyle behaviour change

BACKGROUND. Unhealthy lifestyle behaviours such as physical inactivity are global risk factors for chronic disease. Despite this, a substantial proportion of the UK population fail to achieve the recommended levels of physical activity. This may partly be because the health messages presently disseminated are not sufficiently potent to evoke behaviour change. There has been an exponential growth in the availability of digital health technologies within the consumer marketplace. This influx of technology has allowed people to self-monitor a plethora of health indices, such as their physical activity, in real-time. However, changing movement behaviours is difficult and often predicated on the assumption that individuals are willing to change their lifestyles today to reduce the risk of developing disease years or even decades later. One approach that may help overcome this challenge is to present physiological feedback in parallel with physical activity feedback. In combination, this approach may help people to observe the acute health benefits of being more physically active and subsequently translate that insight into a more physically active lifestyle. AIMS. Study One aimed to review existing studies employing fMRI to examine neurological responses to health messages pertaining to physical activity, sedentary behaviour, smoking, diet and alcohol consumption to assess the capacity for fMRI to assist in evaluating health behaviours. Study Two aimed to use fMRI to evaluate physical activity, sedentary behaviour and glucose feedback obtained through wearable digital health technologies and to explore associations between activated brain regions and subsequent changes in behaviour. Study Three aimed to explore engagement of people at risk of type 2 diabetes using digital health technologies to monitor physical activity and glucose levels. METHODS. Study One was a systematic review of published studies investigating health messages relating to physical activity, sedentary behaviour, diet, smoking or alcohol consumption using fMRI. Study Two asked adults aged 30-60 years to undergo fMRI whilst presented personalised feedback on their physical activity, sedentary behaviour and glucose levels, following a 14-day wear protocol of an accelerometer, inclinometer and flash glucose monitor. Study Three was a six-week, three-armed randomised feasibility trial for individuals at moderate-to-high risk of developing type 2 diabetes. The study used commercially available wearable physical activity (Fitbit Charge 2) and flash glucose (Freestyle Libre) technologies. Group 1 were offered glucose feedback for 4 weeks followed by glucose plus physical activity feedback for 2 weeks (G4GPA2). Group 2 were offered physical activity feedback for 4 weeks followed by glucose plus physical activity feedback for 2 weeks (PA4GPA2). Group 3 were offered glucose plus physical activity feedback for six weeks (GPA6). The primary outcome for the study was engagement, measured objectively by time spent on the Fitbit app, LibreLink app (companion app for the Freestyle Libre) as well as the frequency of scanning the Freestyle Libre and syncing the Fitbit. RESULTS. For Study One, 18 studies were included in the systematic review and of those, 15 examined neurological responses to smoking related health messages. The remaining three studies examined health messages about diet (k=2) and physical activity (k=1). Areas of the prefrontal cortex and amygdala were most commonly activated with increased activation of the ventromedial prefrontal cortex predicting subsequent behaviour (e.g. smoking cessation). Study Two identified that presenting people with personalised feedback relating to interstitial glucose levels resulted in significantly more brain activation when compared with feedback on personalised movement behaviours (P<.001). Activations within regions of the prefrontal cortex were significantly greater for glucose feedback compared with feedback on personalised movement behaviours. Activation in the subgyral area was correlated with moderate-to-vigorous physical activity at follow-up (r=.392, P=.043). In Study Three, time spent on the LibreLink app significantly reduced for G4GPA2 and GPA6 (week 1: 20.2±20 versus week 6: 9.4±14.6min/day, p=.007) and significantly fewer glucose scans were recorded (week 1: 9.2±5.1 versus week 6: 5.9±3.4 scans/day, p=.016). Similarly, Fitbit app usage significantly reduced (week 1: 7.1±3.8 versus week 6: 3.8±2.9min/day p=.003). The number of Fitbit syncs did not change significantly (week 1: 6.9±7.8 versus week 6: 6.5±10.2 syncs/day, p=.752). CONCLUSIONS. Study One highlighted the fact that thus far the field has focused on examining neurological responses to health messages using fMRI for smoking with important knowledge gaps in the neurological evaluation of health messages for other lifestyle behaviours. The prefrontal cortex and amygdala were most commonly activated in response to health messages. Using fMRI, Study Two was able to contribute to the knowledge gaps identified in Study One, with personalised glucose feedback resulting in a greater neurological response than personalised feedback on physical activity and sedentary behaviour. From this, Study Three found that individuals at risk of developing type 2 diabetes were able to engage with digital health technologies offering real-time feedback on behaviour and physiology, with engagement diminishing over time. Overall, this thesis demonstrates the potential for digital health technologies to play a key role in feedback paradigms relating to chronic disease prevention